Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction
This addresses data scarcity in event extraction, particularly for low-resource scenarios, but is incremental as it builds on existing augmentation methods with a more flexible approach.
The paper tackles data augmentation for event extraction by proposing Mask-then-Fill, a framework that masks and infills variable-length text fragments to generate diverse data while preserving event structure. It shows effectiveness over baselines, especially in low-resource settings, with analysis indicating a balance between diversity and distributional similarity.
We present Mask-then-Fill, a flexible and effective data augmentation framework for event extraction. Our approach allows for more flexible manipulation of text and thus can generate more diverse data while keeping the original event structure unchanged as much as possible. Specifically, it first randomly masks out an adjunct sentence fragment and then infills a variable-length text span with a fine-tuned infilling model. The main advantage lies in that it can replace a fragment of arbitrary length in the text with another fragment of variable length, compared to the existing methods which can only replace a single word or a fixed-length fragment. On trigger and argument extraction tasks, the proposed framework is more effective than baseline methods and it demonstrates particularly strong results in the low-resource setting. Our further analysis shows that it achieves a good balance between diversity and distributional similarity.